Now more than ever, African governments need reliable data

Written by Sandra Alba, Christina Mergenthaler and Roland Kielman on 30 April 2020.

At the end of March 2020, in an interview with the BBC, Ellen Johnson Sirleaf, the former president of Liberia, spoke about the 2014-16 Ebola outbreak in West Africa, which killed almost 5,000 Liberians. “Fear drove people to run, to hide, to hoard to protect their own,” she said, “when the only solution is, and remains[,] based in the community”. These wise words take on new resonance in the face of the coronavirus outbreak.

At the time of writing (23 April 2020), the World Health Organization (WHO) has warned that Africa could become “the next epicentre” of the pandemic, with predictions suggesting that the disease could kill at least 300,000 people on the continent (over an unspecified timeframe) while pushing nearly 30 million into poverty. But, as with Ebola before it, the solution to the outbreak lies in the community, as Sirleaf suggests.

For many African countries, community-based surveillance systems present a unique opportunity to gather data about the spread of this disease, allowing governments and public health experts to make evidence-based decisions to fight Covid-19. Now more than ever, countries need reliable data to respond to the pandemic – to inform strategies, monitor their effects, and share lessons learned. African countries should take the opportunity during this pandemic to strengthen community-based surveillance systems, and international development partners should rally to support these efforts.

Keeping check on Covid-19

Despite relatively few confirmed cases across the continent, as of the end of March 2020 a number of African countries had already taken far-reaching measures against Covid-19 – ranging from curfews to total lockdowns. On a continent where millions of people need to leave their homes to earn their daily wage, these measures are expected to have catastrophic socio-economic consequences.

Also, while many African countries have been able to ramp-up their Covid-19 testing capacity, most are not able to keep up with the amount of testing necessary. This is worrying for many public health workers. Without reliable data on the spread of the epidemic, they fear the worst: that their communities will be devastated by the disease and, at the same time, pushed from poverty to starvation by containment measures.

Public health workers across Africa should not have to worry that the epidemic is spiraling out of control in their communities, unknown and unchecked. Decades of investments in health data have provided many nations with a robust health information system that provides the backbone of disease management and surveillance. DHIS2, for example, is the main platform to collect, aggregate, manage, analyse and report public health data in over 30 African countries. DHIS2 has already launched Covid-19 packages which, at the time of writing, have been deployed in Uganda, Ethiopia, Zambia and Malawi.

Most importantly, DHIS2 works well with community reporting systems, which rely on people acting as a direct extension of the health system in their communities (the Covid-19 “case-based surveillance tracker” package is most suitable for this). Community systems are ideal for surveillance based on symptoms and could prove critical to Covid-19 detection in settings where many people do not or cannot seek care when they are ill, and where testing kits are in short supply.

Reporting methods

Community reporting systems range from low-tech pen-and-paper systems to more sophisticated ones using tablets and smartphones. The data flow rests on two pillars: community health workers collect information about symptoms from households, and peripheral health facility staff report these symptoms digitally to a national database. For diseases prone to flaring up in outbreaks, community reporting usually triggers further contact tracing – either by the community health workers themselves or by a closely linked health team.

Community reporting is well established in a number of African countries. Ethiopia is one of the best examples, thanks to its extensive network of community health workers, which has already been deployed to monitor new Covid-19 cases and dispel myths about the disease. Nigeria is another example: the country has leveraged the Avadar system, originally set up to track polio infections, to report Covid-19 symptomatic patients. In other countries, such as Sierra Leone, Guinea and Liberia, community reporting systems were given a huge boost after the 2014-16 Ebola epidemic. Indeed, one of the reasons that epidemic spread so quickly across these countries, ravaging their health systems, was because surveillance at the time was mainly passive, relying on the identification of suspect cases in health facilities. In the aftermath of Ebola, a number of international donors identified community surveillance as being a potential cornerstone of the health systems in these countries and so invested heavily in establishing the necessary surveillance.

With the coronavirus pandemic continuing to spread, African countries can quickly adapt these systems to ensure evidence-based decision making for Covid-19. Those that have an existing system for community-based surveillance should immediately include Covid-19 symptoms in their screening efforts. If community data are fed into DHIS2, they could complement, and potentially even be linked to, any Covid-19 testing data.

Unfortunately, Covid-19 symptoms are rather non-specific, and there a lot of young people in Africa, and young people may be more likely to exhibit only mild symptoms, if any: according to UN estimates, three quarters of the continent’s population is under the age of 35. A satisfactory case definition for Covid-19, rooted in WHO guidance, would have to be developed, striking a balance between medical rigor and practicality for community reporters.

Evaluations by KIT Royal Tropical Institute of community systems in Sierra Leone (focusing on diseases other than Covid-19) have shown that they are not very sensitive (meaning, they are prone to missing cases), but that they do provide a good signal over time and in specific locations. However, one of the main challenges uncovered by these evaluation is that local public health officials can be reluctant to share data with their superiors at the national level to avoid scrutiny and being held accountable for disease outbreaks. Thus, any investment in community systems for Covid-19 must ensure a safe reporting culture.

Systems which interact directly with populations could bypass this sort of challenge. A notable example of such a system is the EU Influenzanet, which allows people to self-screen for influenza-like symptoms. These have already been adapted to the Covid-19 pandemic in most participating countries – with UK examples including Flusurvey and the Covid Symptom Tracker. With many European countries struggling to keep up with testing, these systems are providing useful information on the spread of Covid-19 to national public health institutes. However, their success does depend on high internet or mobile phone penetration rates and thus they may not be feasible in a number of African countries.

The only solution is, and remains, based in the community

Crises can act as formidable catalysts for innovation. Now is the time for African countries to reinforce the progress made in recent decades with disease surveillance.

African health policymakers and development partners should push for locally tailored systems that can be shared and improved across national lines. These will prove essential not only for Covid-19 but also for other diseases – including the next pandemic (whatever it may be).

A strong Covid-19 surveillance system in Africa is crucial not only for nations across the continent but the whole world. Donor countries may be struggling with their own Covid-19 epidemic at home, but they cannot afford to let Africa down in this moment of crisis.

About the authors

Sandra Alba, PhD, CStat, is an epidemiologist at KIT Royal Tropical Institute, with a background in medical statistics. She has 12 years’ experience in the application of statistical and epidemiological methods to evaluate public health programmes, primarily in low- and middle-income countries.

Christina Mergenthaler, MPH, is an epidemiologist at KIT Royal Tropical Institute, specialized in monitoring and evaluation of tuberculosis program data in low-income countries. Currently she is working on an initiative to digitise data collection for tuberculosis active case-finding activities in Pakistan.

Roland Kielman is a communications advisor at KIT Royal Tropical Institute. He holds a master’s degree in political science from the University of Amsterdam and specialises in development, crisis and public policy communications.

Acknowledgements

Many thanks to KIT Royal Tropical Institute colleagues Amdon Weldeabezgi, Yme van den Berg and Mirjam Bakker for insightful comments that helped shape this article. Many thanks also to Venance Zacharia and Hassan Ally for the photograph portraying a TB Reach health worker in Tanzania visiting a household during the Covid-19 epidemic.